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Deep Convolutional Neural Network-Based Analysis for Breast Cancer Histology Images
Abstract
Breast cancer is one of the main causes of cancer death worldwide, and early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious. The relevance and potential of automatic classification algorithms using Hematoxylin-Eosin stained histopathological images have already been demonstrated, but the reported results are still sub-optimal for clinical use. Deep learning-based computer-aided diagnosis (CAD) has been gaining popularity for analyzing histopathological images. Based on the predominant cancer type, the goal is to classify images into four categories of normal, benign, in situ carcinoma, and invasive carcinoma. The convolutional neural networks (CNN) is proposed to retrieve information at different scales, including both nuclei and overall tissue organization. This chapter utilizes several deep neural network architectures and gradient boosted trees classifier to classify the histology images among four classes. Hence, this approach has outperformed existing approaches in terms of accuracy and implementation complexity.
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